In recent years, the structure of global population keeps going towards highly-aged continuously. The development of chronic patient medical care system becomes important and meaningful since people paid a lot attention to medical prevention. The medical care system has to provide alerts in time before the severe chronic illness occurs, such as stroke, diabetics, heart disease. Thus, necessary procedures can be taken in short time to save one precious life. In this paper, we presented a data mining system for chronic patient monitoring with applications on caring of cardiovascular patients. By mining vital signs like ECG the system can predict with a classification tree and inform doctors to take actions if any anomaly could happen. A series of experiments on PAF data showed that our system can stably predict the anomaly from patients' ECG data without coding of medical rules as done in other existing approaches.